analyze the saturation MN kinetics parameter

library(parallel)
library(optimx)
library(GGally)

Registered S3 method overwritten by 'GGally':
  method from   
  +.gg   ggplot2

citation(package = "optimx")

To cite optimx in publications use:

  John C. Nash, Ravi Varadhan (2011). Unifying Optimization Algorithms to Aid Software
  System Users: optimx for R. Journal of Statistical Software, 43(9), 1-14. doi
  10.18637/jss.v043.i09.

  John C. Nash (2014). On Best Practice Optimization Methods in R. Journal of Statistical
  Software, 60(2), 1-14. doi 10.18637/jss.v060.i02.

To see these entries in BibTeX format, use 'print(<citation>, bibtex=TRUE)', 'toBibtex(.)',
or set 'options(citation.bibtex.max=999)'.

list.of.cells <- read_csv("~/plots/all_data/all_exp_data.csv") %>% split(.$cell.id)

Rows: 535515 Columns: 8
── Column specification ────────────────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (5): cell.id, exp.field, degron, red, treatment
dbl (3): delta.time, gfpMeanBgAFsub, image.no

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

#for non treated (no DMSO cells)cells all the three GFPs
bind_rows(list.of.cells) %>% 
  filter( treatment == "none", red == "pup1-rfp") %>% 
  group_by(cell.id) %>% 
  mutate(It_I0 = gfpMeanBgAFsub/gfpMeanBgAFsub[1]) %>% 
  ggplot(.,aes(x = delta.time/60, y = gfpMeanBgAFsub , group_by = cell.id ))+
  geom_line(alpha = 0.2)+
  geom_hline(yintercept = 0.5)+
  facet_wrap(~degron, scales = "free")+
  theme_pubr()

bind_rows(list.of.cells) %>% 
  filter( treatment != "none", red == "pup1-rfp") %>% 
  group_by(cell.id) %>% 
  mutate(It_I0 = gfpMeanBgAFsub/gfpMeanBgAFsub[1]) %>% 
  ggplot(.,aes(x = delta.time/60, y = gfpMeanBgAFsub , group_by = cell.id ))+
  geom_line(alpha = 0.2)+
  geom_hline(yintercept = 0.5)+
  facet_wrap(~treatment, scales = "free")+
  theme_pubr()

#profiles

#CLN2
bind_rows(list.of.cells) %>% 
  filter(degron %in% c("cln2","cln2.2","cln2.3"), treatment == "none", delta.time == 0) %>% 
  ggplot(.,aes(x = gfpMeanBgAFsub, fill = degron))+
  geom_density(aes(y = ..scaled..), alpha = 0.2)+
  facet_wrap(~red, scales = "free_x")+
  scale_x_log10()+
  theme_pubr()

#mODC
bind_rows(list.of.cells) %>% 
  filter(degron %in% c("mODC","mODC.2","mODC.3"), treatment == "none", delta.time == 0) %>% 
   ggplot(.,aes(x = gfpMeanBgAFsub, fill = degron))+
  geom_density(aes(y = ..scaled..), alpha = 0.2)+
  facet_wrap(~red, scales = "free_x")+
  scale_x_log10()+
  theme_pubr()

bind_rows(list.of.cells) %>% 
  filter(image.no == 1) %>% 
  group_by(treatment, red, degron) %>% tally()

mechanistic models with maturation, decay and f

#with 3 parameters (rate of decay, rate of maturation, frac of translation after CHX treatment)
 mechanistic.fn<- function(par, df){
   f <- par[1]
   dy <- par[2]
   dm <- par[3]

   df.new <- df  %>%
     mutate(model = ((dy * (1 - f) * exp(-dy * delta.time)) / dm) + exp(-dy * delta.time) +
              (1 - exp(-dy * delta.time)) * f -
              ((dy * (1 - f) * exp(-(dy + dm) * delta.time)) / dm)) %>%
     mutate(error = (I0_It - model) ^ 2) %>%
     summarise(sum.error = sum(error))
    
    return(df.new$sum.error)
  }

#two parameter model (rate of decay, frac of translation after CHX treatment)
 mechanistic.fn.nodm<- function(par, df){
   f <- par[1]
   dy <- par[2]

   df.new <- df  %>%
     mutate(model = exp(-dy * delta.time) +(1 - exp(-dy * delta.time)) * f ) %>%
     mutate(error = (I0_It - model) ^ 2) %>%
     summarise(sum.error = sum(error))
    
    return(df.new$sum.error)
 }
 
#one parameter model (rate of decay, exponential model)
  mechanistic.fn.exp<- function(par, df){
   
   dy <- par[1]

   df.new <- df  %>%
     mutate(model = exp(-dy * delta.time)) %>%
     mutate(error = (I0_It - model) ^ 2) %>%
     summarise(sum.error = sum(error))
    
    return(df.new$sum.error)
  }
  
#two parameter model (rate of maturation and rate of decay)
    mechanistic.fn.wof<- function(par, df){
  
   dy <- par[1]
   dm <- par[2]

   df.new <- df  %>%
     mutate(model = ((dy  * exp(-dy * delta.time)) / dm) + exp(-dy * delta.time)  -
              ((dy * 1 * exp(-(dy + dm) * delta.time)) / dm)) %>%
     mutate(error = (I0_It - model) ^ 2) %>%
     summarise(sum.error = sum(error))
    
    return(df.new$sum.error)
  }

(dy(1-frac)EXP(-dyA1)/dm + EXP(-dyA1) + (1-EXP(-dyA1))frac - dy(1-frac)EXP(-(dy+dm)*A1)/dm)

log(2)/6#for sfGFP in yeast growth conditions 

[1] 0.1155245

#degron GFPS
list.of.cells.1 <- bind_rows(list.of.cells) %>% #to analyze the new experiments from 7-20-22 and 8-4-22
  filter(degron %in% c("mODC.3","cln2.3")) %>% 
  split(.$cell.id)

#only stable and 50uM treatment cells
list.of.cells.2 <- bind_rows(list.of.cells) %>% #to analyze the stable pup1-rfp from 7-20-22
  filter(degron %in% c("stable", "stable.2") |
         treatment == "50uM") %>% 
  split(.$cell.id)

10-18-22

#degron GFPS
list.of.cells.1 <- bind_rows(list.of.cells) %>% #to analyze the new experiments from 7-20-22 and 8-4-22
  filter(degron == "cln2.4") %>% 
  split(.$cell.id)

#only stable and 50uM treatment cells
list.of.cells.2 <- bind_rows(list.of.cells) %>% #to analyze the stable pup1-rfp from 7-20-22
  filter(degron == "stable.3") %>% 
  split(.$cell.id)

#estimating parameters dy, dm , f

#for the degron GFP

single.cell.dy.f.dm.1 <- mclapply(list.of.cells.1, function(a){ 
   
   par.optim <- c(0.5, 0.05005, 0.1)
   
   names(par.optim) <- c("f", "dy", "dm")
   
   df <- a %>% 
            mutate(image.no = image.no-1) %>% 
            group_by(cell.id) %>% 
            mutate(I0_It = gfpMeanBgAFsub/gfpMeanBgAFsub[1],
                   delta.time = delta.time/60) %>% 
     ungroup()
   
   optimx(par = par.optim, 
          fn = mechanistic.fn , 
          method = "L-BFGS-B", 
          lower = c(0, 0.005, 0.00001), # dy = 0.00001*60 for stable GFP
          upper = c(1, 1, Inf), #changed from dy = 6 to dy = 10 and now to dy = 1
          df = df,
          itnmax = 100000)
   }, mc.cores = 40)

#for the stable GFP and the 50uM treatment
#for the repeate of 10-7-22 
single.cell.dy.f.dm.2 <- mclapply(list.of.cells.2, function(a){ 
   
   par.optim <- c(0.5, 0.005005, 0.1)
   
   names(par.optim) <- c("f", "dy", "dm")
   
   df <- a %>% 
            mutate(image.no = image.no-1) %>% 
            group_by(cell.id) %>% 
            mutate(I0_It = gfpMeanBgAFsub/gfpMeanBgAFsub[1],
                   delta.time = delta.time/60) %>% 
     ungroup()
   
   optimx(par = par.optim, 
          fn = mechanistic.fn , 
          method = "L-BFGS-B", 
          lower = c(0, 0.0005, 0.00001), # dy = 0.00001*60 for stable GFP
          upper = c(1, 0.1, Inf), #change dy = 0.1 from 10, similarly for all the other optimizations 
          df = df,
          itnmax = 100000)
   }, mc.cores = 40)

single.cell.dy.f.dm.df <- single.cell.dy.f.dm %>% bind_rows(.id = "cell.id")
single.cell.dy.f.dm.df.2 <- single.cell.dy.f.dm.2 %>% bind_rows(.id = "cell.id")

converged.cells.old <- single.cell.dy.f.dm.df  %>% 
  filter(convcode == 0) %>% 
  mutate(degron = str_split(cell.id, "_", simplify = T)[,4], 
         red = str_split(cell.id, "_", simplify = T)[,5],
         treatment = str_split(cell.id, "_", simplify = T)[,6])

plots

#dis of f
converged.cells %>% 
  ggplot(.,aes(x = f, color = treatment))+
  geom_density(aes(y = ..scaled..))+
  facet_grid(red~degron)

#dm
converged.cells %>% 
  ggplot(.,aes(x = dm, color = treatment))+
  geom_density(aes(y = ..scaled..))+
  facet_grid(red~degron)+
  scale_x_log10()

#dy
converged.cells %>% 
  ggplot(.,aes(x = dy, color = treatment))+
  geom_density(aes(y = ..scaled..))+
  facet_grid(red~degron)+
  scale_x_log10()

#dy
# converged.cells.old %>% 
#   ggplot(.,aes(x = dy, color = treatment))+
#   geom_density(aes(y = ..scaled..))+
#   facet_grid(red~degron)+
#   scale_x_log10()

#half lives
converged.cells %>% 
  ggplot(.,aes(x = log(2)/dy, color = treatment))+
  geom_density(aes(y = ..scaled..))+
  facet_grid(red~degron)

#maturation times
converged.cells %>% 
  filter(treatment == "none") %>% 
  ggplot(.,aes(x = log(2)/dm, color = treatment))+
  geom_density(aes(y = ..scaled..))+
  facet_grid(red~degron)+
  scale_x_log10()

#RSS
converged.cells %>% 
  ggplot(.,aes(x = value, color = treatment))+
  geom_density(aes(y = ..scaled..))+
  facet_grid(red~degron)+
  scale_x_log10()

Average values of the parameters

converged.cells %>% 
  group_by(treatment, red, degron) %>% 
  summarise(dm.avg = mean(dm))

`summarise()` has grouped output by 'treatment', 'red'. You can override using the `.groups` argument.

converged.cells %>% 
  group_by(treatment, red, degron) %>% 
  summarise(f.avg = mean(f),
            dy.avg = mean(dy))

`summarise()` has grouped output by 'treatment', 'red'. You can override using the `.groups` argument.

#without dm just dy and f

#for the degron GFP
single.cell.dy.f.1 <- mclapply(list.of.cells.1, function(a){ 
   
   par.optim <- c(0.5, 0.05005)
   
   names(par.optim) <- c("f", "dy")
   
   df <- a %>% 
            mutate(image.no = image.no-1) %>% 
            group_by(cell.id) %>% 
            mutate(I0_It = gfpMeanBgAFsub/gfpMeanBgAFsub[1],
                   delta.time = delta.time/60) %>% 
     ungroup()
   
   optimx(par = par.optim, #updated this line to match the initial db with exp.field when I am optimizing in a loop
          fn = mechanistic.fn.nodm , 
          method = "L-BFGS-B", 
          lower = c(0, 0.005), 
          upper = c(1, 1), 
          df = df,
          itnmax = 100000)
   }, mc.cores = 40)

#for the stable GFP and the 50uM treatment 
single.cell.dy.f.2<- mclapply(list.of.cells.2, function(a){ 
   
   par.optim <- c(0.5, 0.005005)
   
   names(par.optim) <- c("f", "dy")
   
   df <- a %>% 
            mutate(image.no = image.no-1) %>% 
            group_by(cell.id) %>% 
            mutate(I0_It = gfpMeanBgAFsub/gfpMeanBgAFsub[1],
                   delta.time = delta.time/60) %>% 
     ungroup()
   
   optimx(par = par.optim, #updated this line to match the initial db with exp.field when I am optimizing in a loop
          fn = mechanistic.fn.nodm , 
          method = "L-BFGS-B", 
          lower = c(0, 0.0005), 
          upper = c(1, 0.1), 
          df = df,
          itnmax = 100000)
   }, mc.cores = 40)

single.cell.dy.f.df.1 <- single.cell.dy.f.1 %>% 
  bind_rows(.id = "cell.id")

single.cell.dy.f.df.2 <- single.cell.dy.f.2 %>% 
  bind_rows(.id = "cell.id")

converged.cells.2 <- bind_rows(single.cell.dy.f.df.1,
                               single.cell.dy.f.df.2) %>% 
  filter(convcode == 0) %>% 
  mutate(degron = str_split(cell.id, "_", simplify = T)[,4], 
         red = str_split(cell.id, "_", simplify = T)[,5],
         treatment = str_split(cell.id, "_", simplify = T)[,6])

converged.cells.2.old <- single.cell.dy.f.df %>% 
  filter(convcode == 0) %>% 
  mutate(degron = str_split(cell.id, "_", simplify = T)[,4], 
         red = str_split(cell.id, "_", simplify = T)[,5],
         treatment = str_split(cell.id, "_", simplify = T)[,6])

plots

#exponential

#for degron GFPs
single.cell.dy.1<- mclapply(list.of.cells.1, function(a){ 
   
   par.optim <- c(0.05005)
   
   names(par.optim) <- c("dy")
   
   df <- a %>% 
            mutate(image.no = image.no-1) %>% 
            group_by(cell.id) %>% 
            mutate(I0_It = gfpMeanBgAFsub/gfpMeanBgAFsub[1],
                   delta.time = delta.time/60) %>% 
     ungroup()
   
   optimx(par = par.optim, #updated this line to match the initial db with exp.field when I am optimizing in a loop
          fn = mechanistic.fn.exp , 
          method = "L-BFGS-B", 
          lower = c(0.005), 
          upper = c(1), 
          df = df,
          itnmax = 100000)
   }, mc.cores = 40)

#for stable gfp and 50uM 
single.cell.dy.2 <- mclapply(list.of.cells.2, function(a){ 
   
   par.optim <- c(0.005005)
   
   names(par.optim) <- c("dy")
   
   df <- a %>% 
            mutate(image.no = image.no-1) %>% 
            group_by(cell.id) %>% 
            mutate(I0_It = gfpMeanBgAFsub/gfpMeanBgAFsub[1],
                   delta.time = delta.time/60) %>% 
     ungroup()
   
   optimx(par = par.optim, #updated this line to match the initial db with exp.field when I am optimizing in a loop
          fn = mechanistic.fn.exp , 
          method = "L-BFGS-B", 
          lower = c(0.0005), 
          upper = c(0.1), 
          df = df,
          itnmax = 100000)
   }, mc.cores = 40)

single.cell.dy.df.1 <- single.cell.dy.1 %>% 
  bind_rows(.id = "cell.id")

single.cell.dy.df.2 <- single.cell.dy.2 %>% 
  bind_rows(.id = "cell.id")

converged.cells.3 <- bind_rows(single.cell.dy.df.1,
                               single.cell.dy.df.2) %>% 
  filter(convcode == 0) %>% 
  mutate(degron = str_split(cell.id, "_", simplify = T)[,4], 
         red = str_split(cell.id, "_", simplify = T)[,5],
         treatment = str_split(cell.id, "_", simplify = T)[,6])

converged.cells.3.old <- single.cell.dy.df %>% 
  filter(convcode == 0) %>% 
  mutate(degron = str_split(cell.id, "_", simplify = T)[,4], 
         red = str_split(cell.id, "_", simplify = T)[,5],
         treatment = str_split(cell.id, "_", simplify = T)[,6])

plots

#dy
converged.cells.3 %>% 
  ggplot(.,aes(x = dy, color = treatment))+
  geom_density(aes(y = ..scaled..))+
  facet_grid(red~degron)+
  scale_x_log10() +
  theme(axis.text.x  = element_text(angle = 45))

converged.cells.3.old %>% 
  ggplot(.,aes(x = dy, color = treatment))+
  geom_density(aes(y = ..scaled..))+
  facet_grid(red~degron)+
  scale_x_log10()+
  theme(axis.text.x  = element_text(angle = 45))

#half lives
converged.cells.3 %>% 
  ggplot(.,aes(x = log(2)/dy, color = treatment))+
  geom_density(aes(y = ..scaled..))+
  facet_grid(red~degron)

#rss
converged.cells.3 %>% 
  ggplot(.,aes(x = value, color = treatment))+
  geom_density(aes(y = ..scaled..))+
  facet_grid(red~degron)+
  scale_x_log10()

#without f just dm and dy

single.cell.dy.dm.1 <- mclapply(list.of.cells.1, function(a){ 
   
   par.optim <- c(0.05005, 0.1)
   
   names(par.optim) <- c("dy", "dm")
   
   df <- a %>% 
            mutate(image.no = image.no-1) %>% 
            group_by(cell.id) %>% 
            mutate(I0_It = gfpMeanBgAFsub/gfpMeanBgAFsub[1],
                   delta.time = delta.time/60) %>% 
     ungroup()
   
   optimx(par = par.optim, #updated this line to match the initial db with exp.field when I am optimizing in a loop
          fn = mechanistic.fn.wof, 
          method = "L-BFGS-B", 
          lower = c( 0.005, 0.00001), 
          upper = c( 1, Inf), 
          df = df,
          itnmax = 100000)
   }, mc.cores = 40)

#stable gfp
single.cell.dy.dm.2 <- mclapply(list.of.cells.2, function(a){ 
   
   par.optim <- c(0.005, 0.1)
   
   names(par.optim) <- c("dy", "dm")
   
   df <- a %>% 
            mutate(image.no = image.no-1) %>% 
            group_by(cell.id) %>% 
            mutate(I0_It = gfpMeanBgAFsub/gfpMeanBgAFsub[1],
                   delta.time = delta.time/60) %>% 
     ungroup()
   
   optimx(par = par.optim, #updated this line to match the initial db with exp.field when I am optimizing in a loop
          fn = mechanistic.fn.wof, 
          method = "L-BFGS-B", 
          lower = c( 0.0005, 0.00001), 
          upper = c( 0.1, Inf), 
          df = df,
          itnmax = 100000)
   }, mc.cores = 40)

single.cell.dy.dm.df.1 <- single.cell.dy.dm.1 %>% bind_rows(.id = "cell.id")
single.cell.dy.dm.df.2 <- single.cell.dy.dm.2 %>% bind_rows(.id = "cell.id")

converged.cells.4.old <- single.cell.dy.dm.df %>% 
  filter(convcode == 0) %>% 
  mutate(degron = str_split(cell.id, "_", simplify = T)[,4], 
         red = str_split(cell.id, "_", simplify = T)[,5],
         treatment = str_split(cell.id, "_", simplify = T)[,6])

converged.cells.4 <- bind_rows(single.cell.dy.dm.df.1,
                               single.cell.dy.dm.df.2) %>% 
  filter(convcode == 0) %>% 
  mutate(degron = str_split(cell.id, "_", simplify = T)[,4], 
         red = str_split(cell.id, "_", simplify = T)[,5],
         treatment = str_split(cell.id, "_", simplify = T)[,6])

plots

dm vs gfp intensity

AIC function equation from the paper: McShane et al 2016 : Kinetic analysis of protein degradation reveals age dependent degradation.

#for a small sample space, you add a correction 2k(K+1)/(n-k-1) AIC = 2k + n log(RSS/n) + 2k(K+1)/(n-k-1)

n = no. of data points (31) k = no. of parameters c(1,2,3) RSS = Residual sum of squares

exp((AICmin − AICi)/2) can be interpreted as being proportional to the probability that the ith model minimizes the (estimated) information loss.

aic.fn <- function(df,n){
  
  df <- df %>% 
    mutate(aic = 2*k + n*log(value/n) ) #without the correction for small sample space
  return(df)
}

Old values

#for three par model
aic.dy.f.dm.old <- aic.fn(converged.cells.old, k = 3, n = 31) %>% 
  rename("dy.all" = "dy", 
         "f.all" = "f",
         "dm.all" = "dm",
         "rss.all" = "value") %>% 
  select(cell.id, dy.all, f.all, dm.all, rss.all, aic.all, treatment, red, degron)

#for dy and f model only
aic.dy.f.old <- aic.fn(converged.cells.2.old, k = 2, n = 31) %>% 
  rename("dy.2" = "dy", 
         "f.2" = "f",
         "rss.2" = "value",
         "aic.dy.f" = "aic.all")  %>% 
  select(cell.id, dy.2, f.2, rss.2, aic.dy.f,treatment, red, degron)

#exponential
aic.dy.old <- aic.fn(converged.cells.3.old, k = 1, n = 31) %>% 
  rename("dy.exp" = "dy", 
         "rss.exp" = "value",
         "aic.exp" = "aic.all") %>% 
  select(cell.id, dy.exp, rss.exp, aic.exp,treatment, red, degron)

#mat and decay
aic.dy.dm.old <- aic.fn(converged.cells.4.old, k = 2, n = 31) %>%  
  rename("dy.mat" = "dy", 
         "dm.mat" = "dm",
         "rss.mat" = "value",
         "aic.mat" = "aic.all")  %>% 
  select(cell.id, dy.mat, dm.mat,rss.mat, aic.mat,treatment, red, degron)

all.model.parm <- all.model.parm %>% 
  mutate(k = case_when(model == "dy.dm.f" ~ 3,
                       model %in% c("dy.f","dy.dm") ~ 2,
                       model == "exponential" ~ 1))

aic.df <- aic.fn(all.model.parm, n = 31) %>% 
  group_by(cell.id) %>% 
  arrange(cell.id) %>% 
  ungroup()

Comparing AIC values

aic.df %>%
  filter(treatment %in% c("none","dmso1","dmso2")) %>% 
  ggplot(.,aes(x = aic.all, y = aic.dy.f, color = treatment))+
  geom_point(alpha = 0.2)+
  geom_abline(slope = 1)+
  facet_grid(red~degron, scales = "free")

aic.df %>%
  filter(treatment %in% c("none","dmso1","dmso2")) %>% 
  ggplot(.,aes(x = aic.all, y = aic.exp, color = treatment))+
  geom_point(alpha = 0.2)+
  geom_abline(slope = 1)+
  facet_grid(red~degron)

aic.df %>%
  filter(treatment %in% c("none","dmso1","dmso2")) %>% 
  ggplot(.,aes(x = aic.all, y = aic.mat, color = treatment))+
  geom_point(alpha = 0.2)+
  geom_abline(slope = 1)+
  facet_grid(red~degron)

#for the treatment exps
aic.df %>%
  filter(treatment != "none") %>% 
  ggplot(.,aes(x = aic.all, y = aic.mat))+
  geom_point(alpha = 0.2)+
  geom_abline(slope = 1)+
  facet_wrap(~treatment)

aic.df %>%
  filter(treatment != "none") %>% 
  ggplot(.,aes(x = aic.all, y = aic.dy.f))+
  geom_point(alpha = 0.2)+
  geom_abline(slope = 1)+
  facet_wrap(~treatment)

aic.df %>%
  filter(treatment != "none") %>% 
  ggplot(.,aes(x = aic.all, y = aic.exp))+
  geom_point(alpha = 0.2)+
  geom_abline(slope = 1)+
  facet_wrap(~treatment)

NA
NA

#repeate 2
aic.df %>% 
  filter(degron == "cln2.2" & red == "pup1-rfp") %>% 
  select(contains("aic")) %>% 
  ggpairs()

 plot: [1,1] [====>--------------------------------------------------------------------]  6% est: 0s 
 plot: [1,2] [========>----------------------------------------------------------------] 12% est: 1s 
 plot: [1,3] [=============>-----------------------------------------------------------] 19% est: 1s 
 plot: [1,4] [=================>-------------------------------------------------------] 25% est: 1s 
 plot: [2,1] [======================>--------------------------------------------------] 31% est: 1s 
 plot: [2,2] [==========================>----------------------------------------------] 38% est: 1s 
 plot: [2,3] [===============================>-----------------------------------------] 44% est: 1s 
 plot: [2,4] [===================================>-------------------------------------] 50% est: 1s 
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#repeate 1
aic.df %>% 
  filter(degron == "cln2" & red == "pup1-rfp") %>% 
  select(contains("aic")) %>% 
  ggpairs()

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aic.df %>% 
  filter(degron == "cln2.2" & red == "tef2-mCherry") %>% 
  select(contains("aic")) %>% 
  ggpairs()

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 plot: [4,3] [===================================================================>-----] 94% est: 0s 
 plot: [4,4] [=========================================================================]100% est: 0s 
                                                                                                     

aic.df %>% 
  filter(degron == "cln2" & red == "tef2-mCherry") %>% 
  select(contains("aic")) %>% 
  ggpairs()

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aic.df %>% 
  filter(degron == "mODC.2" & red == "pup1-rfp") %>% 
  select(contains("aic")) %>% 
  ggpairs()

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aic.df %>% 
  filter(degron == "mODC" & red == "pup1-rfp", treatment == "none") %>% 
  select(contains("aic")) %>% 
  ggpairs()

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 plot: [4,3] [===================================================================>-----] 94% est: 0s 
 plot: [4,4] [=========================================================================]100% est: 0s 
                                                                                                     

aic.df %>% 
  filter(degron == "mODC.2" & red == "tef2-mCherry") %>% 
  select(contains("aic")) %>% 
  ggpairs()

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 plot: [4,3] [===================================================================>-----] 94% est: 0s 
 plot: [4,4] [=========================================================================]100% est: 0s 
                                                                                                     

aic.df %>% 
  filter(degron == "mODC" & red == "tef2-mCherry") %>% 
  select(contains("aic")) %>% 
  ggpairs()

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Distribution of the best fit model

comparing the rates of decay from various models

aic.df %>%
  filter(treatment %in% c("none","dmso1","dmso2"), 
         dy.all < 1, dy.2 <1, dy.exp <1 , dy.mat <1) %>% 
  ggplot(.,aes(x = dy.all, y = dy.2, color = treatment))+
  geom_point(alpha = 0.2)+
  geom_abline(slope = 1)+
  facet_grid(red~degron, scales = "free")+
  scale_x_log10()+
  scale_y_log10()+
  xlab("rate of decay from the 3 par model")+
  ylab("rate of decay from 2 par model (f, dy)")

aic.df %>%
  filter(treatment %in% c("none","dmso1","dmso2"), 
         dy.all < 1, dy.2 <1, dy.exp <1 , dy.mat <1)  %>% 
  ggplot(.,aes(x = dy.all, y = dy.exp, color = treatment))+
  geom_point(alpha = 0.2)+
  geom_abline(slope = 1)+
  facet_grid(red~degron, scales = "free")+
  scale_x_log10()+
  scale_y_log10()+
  xlab("rate of decay from the 3 par model")+
  ylab("rate of decay from 1 par model ( dy)")

aic.df %>%
  filter(treatment %in% c("none","dmso1","dmso2"), 
         dy.all < 1, dy.2 <1, dy.exp <1 , dy.mat <1) %>% 
  ggplot(.,aes(x = dy.all, y = dy.mat, color = treatment))+
  geom_point(alpha = 0.2)+
  geom_abline(slope = 1)+
  facet_grid(red~degron, scales = "free")+
  scale_x_log10()+
  scale_y_log10()+
  xlab("rate of decay from the 3 par model")+
  ylab("rate of decay from 2 par model ( dy, dm)")

maturation rates

aic.df %>%
  filter(treatment %in% c("none","dmso1","dmso2"), 
         dy.all < 1, dy.2 <1, dy.exp <1 , dy.mat <1) %>% 
  ggplot(.,aes(x = dm.all, y = dm.mat, color = treatment))+
  geom_point(alpha = 0.2)+
  geom_abline(slope = 1)+
  facet_grid(red~degron, scales = "free")+
  scale_x_log10()+
  scale_y_log10()+
  xlab("rate of maturation from the 3 par model")+
  ylab("rate of maturation from 2 par model ( dy, dm)")

f values

aic.df %>%
  filter(treatment %in% c("none"), 
         dy.all < 1, dy.2 <1, dy.exp <1 , dy.mat <1) %>% 
  ggplot(.,aes(x = f.all, y = f.2, color = treatment))+
  geom_point(alpha = 0.2)+
  geom_abline(slope = 1)+
  facet_grid(red~degron, scales = "free")+
  xlab("frac of translation from the 3 par model")+
  ylab("frac of translation from 2 par model ( dy, f)")

aic.df %>%
  filter(treatment %in% c("none"), 
         dy.all < 1, dy.2 <1, dy.exp <1 , dy.mat <1) %>% 
  group_by(treatment, red, degron) %>% 
  summarise(avg.dy.all = mean(dy.all),
            avg.dy.2 = mean(dy.2),
            avg.dy.exp = mean(dy.exp),
            avg.dy.mat = mean(dy.mat))

#decay rate distribution plots

aic.df %>%
  filter(treatment %in% c("none"), 
         dy.all < 1, dy.2 <1, dy.exp <1 , dy.mat <1) %>% 
  select(cell.id, dy.all, dy.2, dy.exp, dy.mat, treatment, red, degron) %>% 
  pivot_longer(cols = 2:5) %>% 
  ggplot(.,aes(x = value,  fill = name))+
  geom_histogram( alpha = 0.5)+
  facet_grid(red~degron, scales = "free")+
  scale_x_log10()+
  theme_pubr()

`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

aic.df %>%
  filter(treatment %in% c("none"), 
         dy.all < 1, dy.2 <1, dy.exp <1 , dy.mat <1 , 
         degron == "cln2.2") %>% 
  select(cell.id, dy.all, dy.2, dy.exp, dy.mat, treatment, red, degron) %>% 
  pivot_longer(cols = 2:5) %>% 
  ggplot(.,aes(x = log(2)/value,  fill = name))+
  geom_density( aes(y = ..scaled..),alpha = 0.5)+
  facet_grid(red~degron, scales = "free")+
  theme_pubr()+
  scale_x_continuous()

aic.df %>%
  filter(treatment %in% c("none"), 
         dy.all < 1, dy.2 <1, dy.exp <1 , dy.mat <1 , 
         degron %in% c("mODC","mODC.2") ) %>% 
  select(cell.id, dy.all, dy.2, dy.exp, dy.mat, treatment, red, degron) %>% 
  pivot_longer(cols = 2:5) %>% 
  ggplot(.,aes(x = log(2)/value,  fill = name))+
  geom_density( aes(y = ..scaled..),alpha = 0.5)+
  facet_grid(red~degron, scales = "free")+
  theme_pubr()+
  scale_x_continuous()

NA

aic.df %>%
  filter(treatment %in% c("none"), 
         dy.all < 1, dy.2 <1, dy.exp <1 , dy.mat <1 , 
         degron == "cln2.2", 
         red == "pup1-rfp") %>% 
  select(cell.id, dy.all, dy.2, dy.exp, dy.mat, treatment, red, degron) %>% 
  select(contains("dy")) %>% 
  ggpairs()

 plot: [1,1] [====>--------------------------------------------------------------------]  6% est: 0s 
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 plot: [3,4] [======================================================>------------------] 75% est: 0s 
 plot: [4,1] [==========================================================>--------------] 81% est: 0s 
 plot: [4,2] [===============================================================>---------] 88% est: 0s 
 plot: [4,3] [===================================================================>-----] 94% est: 0s 
 plot: [4,4] [=========================================================================]100% est: 0s 
                                                                                                     

aic.df %>%
  filter(treatment %in% c("none"), 
         dy.all < 1, dy.2 <1, dy.exp <1 , dy.mat <1 , 
         degron == "cln2.2", 
         red == "tef2-mCherry") %>% 
  select(cell.id, dy.all, dy.2, dy.exp, dy.mat, treatment, red, degron) %>% 
  select(contains("dy")) %>% 
  ggpairs()

 plot: [1,1] [====>--------------------------------------------------------------------]  6% est: 0s 
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 plot: [3,3] [=================================================>-----------------------] 69% est: 0s 
 plot: [3,4] [======================================================>------------------] 75% est: 0s 
 plot: [4,1] [==========================================================>--------------] 81% est: 0s 
 plot: [4,2] [===============================================================>---------] 88% est: 0s 
 plot: [4,3] [===================================================================>-----] 94% est: 0s 
 plot: [4,4] [=========================================================================]100% est: 0s 
                                                                                                     

aic.df %>%
  filter(treatment %in% c("none"), 
         dy.all < 1, dy.2 <1, dy.exp <1 , dy.mat <1 , 
         degron == "mODC.2", 
         red == "pup1-rfp") %>% 
  select(cell.id, dy.all, dy.2, dy.exp, dy.mat, treatment, red, degron) %>% 
  select(contains("dy")) %>% 
  ggpairs()

 plot: [1,1] [====>--------------------------------------------------------------------]  6% est: 0s 
 plot: [1,2] [========>----------------------------------------------------------------] 12% est: 1s 
 plot: [1,3] [=============>-----------------------------------------------------------] 19% est: 1s 
 plot: [1,4] [=================>-------------------------------------------------------] 25% est: 1s 
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 plot: [3,1] [========================================>--------------------------------] 56% est: 0s 
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 plot: [4,1] [==========================================================>--------------] 81% est: 0s 
 plot: [4,2] [===============================================================>---------] 88% est: 0s 
 plot: [4,3] [===================================================================>-----] 94% est: 0s 
 plot: [4,4] [=========================================================================]100% est: 0s 
                                                                                                     

aic.df %>%
  filter(treatment %in% c("none"), 
         dy.all < 1, dy.2 <1, dy.exp <1 , dy.mat <1 , 
         degron == "mODC.2", 
         red == "tef2-mCherry") %>% 
  select(cell.id, dy.all, dy.2, dy.exp, dy.mat, treatment, red, degron) %>% 
  select(contains("dy")) %>% 
  ggpairs()

 plot: [1,1] [====>--------------------------------------------------------------------]  6% est: 0s 
 plot: [1,2] [========>----------------------------------------------------------------] 12% est: 0s 
 plot: [1,3] [=============>-----------------------------------------------------------] 19% est: 0s 
 plot: [1,4] [=================>-------------------------------------------------------] 25% est: 0s 
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 plot: [3,1] [========================================>--------------------------------] 56% est: 0s 
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 plot: [3,3] [=================================================>-----------------------] 69% est: 0s 
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 plot: [4,1] [==========================================================>--------------] 81% est: 0s 
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 plot: [4,3] [===================================================================>-----] 94% est: 0s 
 plot: [4,4] [=========================================================================]100% est: 0s 
                                                                                                     

when you add proteasome inhibtor, there

#Saving the data

read_csv("~/plots/all_data/aic.csv") %>% 
  bind_rows(.,aic.df) %>% 
  write_csv(.,path = "~/plots/all_data/aic.csv")

Rows: 55379 Columns: 18
── Column specification ───────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr  (5): cell.id, degron, red, treatment, model
dbl (10): f, dy, dm, value, fevals, gevals, convcode, xtime, k, aic
lgl  (3): niter, kkt1, kkt2

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

# all.model.parm %>% 
#   write_csv(.,"~/plots/all_data/model_parms_2.csv")

# all.model.parm2 <- read_csv("~/plots/all_data/model_parms_2.csv") %>% 
#   filter(!(degron %in% c("stable", "stable.2") | treatment == "50uM")) %>% 
#   bind_rows(.,all.model.parm) 
#   
#   
# all.model.parm2 %>% 
#   write_csv(.,"~/plots/all_data/model_parms_2.csv")

#10-18-22
read_csv("~/plots/all_data/model_parms_2.csv") %>% 
  bind_rows(.,all.model.parm) %>% 
  write_csv(.,"~/plots/all_data/model_parms_2.csv")

decay rate distribution with maturation model

aic.df %>%
  filter(treatment %in% c("none"), 
         dy.all < 1, dy.2 <1, dy.exp <1 , dy.mat <1 , 
         degron == "cln2.2") %>% 
  select(cell.id, dy.all, dy.2, dy.exp, dy.mat, treatment, red, degron) %>% 
  pivot_longer(cols = 2:5) %>% 
  ggplot(.,aes(x = log(2)/value,  fill = name))+
  geom_density( aes(y = ..scaled..),alpha = 0.5)+
  facet_grid(red~degron, scales = "free")+
  theme_pubr()+
  scale_x_continuous()

Error in `filter()`:
! Problem while computing `..2 = dy.all < 1`.
Caused by error in `mask$eval_all_filter()`:
! object 'dy.all' not found
Backtrace:
  1. ... %>% ggplot(., aes(x = log(2) / value, fill = name))
  6. dplyr:::filter.data.frame(...)
  7. dplyr:::filter_rows(.data, ..., caller_env = caller_env())
  8. dplyr:::filter_eval(dots, mask = mask, error_call = error_call)
 10. mask$eval_all_filter(dots, env_filter)

aic.df %>%
  filter(treatment %in% c("none"), 
         dy.all < 1, dy.2 <1, dy.exp <1 , dy.mat <1 , 
         degron %in% c("mODC","mODC.2") ) %>% 
  select(cell.id, dy.mat, treatment, red, degron) %>% 
  left_join(.,bind_rows(list.of.cells) %>% filter(delta.time == 0), by = c("cell.id","red","degron","treatment")) %>% 
  ggplot(.,aes(y = dy.mat, x = gfpMeanBgAFsub, color = degron))+
  geom_point( alpha = 0.5)+
  facet_wrap(~red, scales = "free")+
  theme_pubr()+
  scale_x_continuous()+
  stat_cor()

aic.df %>%
  filter(treatment %in% c("none"), 
         dy.all < 1, dy.2 <1, dy.exp <1 , dy.mat <1 , 
         degron %in% c("mODC","mODC.2") ) %>% 
  select(cell.id, dy.2, treatment, red, degron) %>% 
  left_join(.,bind_rows(list.of.cells) %>% filter(delta.time == 0), by = c("cell.id","red","degron","treatment")) %>% 
  ggplot(.,aes(y = dy.2, x = gfpMeanBgAFsub, color = degron))+
  geom_point( alpha = 0.5)+
  facet_wrap(~red, scales = "free")+
  theme_pubr()+
  scale_x_continuous()+
  stat_cor()

aic.df %>%
  filter(treatment %in% c("none"), 
         dy.all < 1, dy.2 <1, dy.exp <1 , dy.mat <1 , 
         degron %in% c("mODC","mODC.2") ) %>% 
  select(cell.id, dy.all, treatment, red, degron) %>% 
  left_join(.,bind_rows(list.of.cells) %>% filter(delta.time == 0), by = c("cell.id","red","degron","treatment")) %>% 
  ggplot(.,aes(y = dy.all, x = gfpMeanBgAFsub, color = degron))+
  geom_point( alpha = 0.5)+
  facet_wrap(~red, scales = "free")+
  theme_pubr()+
  scale_x_continuous()+
  stat_cor()

aic.df %>%
  filter(treatment %in% c("none"), 
         dy.all < 1, dy.2 <1, dy.exp <1 , dy.mat <1 , 
         degron %in% c("mODC","mODC.2") ) %>% 
  select(cell.id, dy.exp, treatment, red, degron) %>% 
  left_join(.,bind_rows(list.of.cells) %>% filter(delta.time == 0), by = c("cell.id","red","degron","treatment")) %>% 
  ggplot(.,aes(y = dy.exp, x = gfpMeanBgAFsub, color = degron))+
  geom_point( alpha = 0.5)+
  facet_wrap(~red, scales = "free")+
  theme_pubr()+
  scale_x_continuous()+
  stat_cor()

 
aic.df %>%
  filter(treatment %in% c("none"), 
         dy.all < 1, dy.2 <1, dy.exp <1 , dy.mat <1 , 
         degron %in% c("cln2","cln2.2") ) %>% 
  select(cell.id, dy.mat, treatment, red, degron) %>% 
  left_join(.,bind_rows(list.of.cells) %>% filter(delta.time == 0), by = c("cell.id","red","degron","treatment")) %>% 
  ggplot(.,aes(y = dy.mat, x = gfpMeanBgAFsub, color = degron))+
  geom_point( alpha = 0.5)+
  facet_grid(~red, scales = "free")+
  theme_pubr()+
  scale_x_continuous()+
  stat_cor()

aic.df %>%
  filter(treatment %in% c("none"), 
         dy.all < 1, dy.2 <1, dy.exp <1 , dy.mat <1 , 
         degron %in% c("cln2","cln2.2") ) %>% 
  select(cell.id, dy.2, treatment, red, degron) %>% 
  left_join(.,bind_rows(list.of.cells) %>% filter(delta.time == 0), by = c("cell.id","red","degron","treatment")) %>% 
  ggplot(.,aes(y = dy.2, x = gfpMeanBgAFsub, color = degron))+
  geom_point( alpha = 0.5)+
  facet_grid(~red, scales = "free")+
  theme_pubr()+
  scale_x_continuous()+
  stat_cor()

aic.df %>%
  filter(treatment %in% c("none"), 
         dy.all < 1, dy.2 <1, dy.exp <1 , dy.mat <1 , 
         degron %in% c("cln2","cln2.2") ) %>% 
  select(cell.id, dy.exp, treatment, red, degron) %>% 
  left_join(.,bind_rows(list.of.cells) %>% filter(delta.time == 0), by = c("cell.id","red","degron","treatment")) %>% 
  ggplot(.,aes(y = dy.exp, x = gfpMeanBgAFsub, color = degron))+
  geom_point( alpha = 0.5)+
  facet_grid(~red, scales = "free")+
  theme_pubr()+
  scale_x_continuous()+
  stat_cor()

aic.df %>%
  filter(treatment %in% c("none"), 
         dy.all < 1, dy.2 <1, dy.exp <1 , dy.mat <1 , 
         degron %in% c("cln2","cln2.2") ) %>% 
  select(cell.id, dy.all, treatment, red, degron) %>% 
  left_join(.,bind_rows(list.of.cells) %>% filter(delta.time == 0), by = c("cell.id","red","degron","treatment")) %>% 
  ggplot(.,aes(y = dy.all, x = gfpMeanBgAFsub, color = degron))+
  geom_point( alpha = 0.5)+
  facet_grid(~red, scales = "free")+
  theme_pubr()+
  scale_x_continuous()+
  stat_cor()